Learning from multiple analogies: an Information Theoretic framework for predicting criminal recidivism

If recidivism is defined as rearrest within a finite period following release from prison, then the kinds of outcomes typically available to researchers include: (i) whether or not the individual was rearrested within the follow-up period; (ii) how many times the individual was rearrested; and (iii) what was the duration from release to first (or subsequent) rearrest. Since these outcomes are all different manifestations of the same underlying stochastic process, they provide multiple analogies from which to recover information about it. This paper develops a semi-parametric approach for utilizing information in these, and several other related outcomes, to predict criminal recidivism and presents preliminary findings.